Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy
نویسندگان
چکیده
منابع مشابه
Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy
Markov Chain Monte Carlo based Bayesian data analysis has now become the method of choice for analyzing and interpreting data in almost all disciplines of science. In astronomy, over the last decade, we have also seen a steady increase in the number of papers that employ Monte Carlo based Bayesian analysis. New, efficient Monte Carlo based methods are continuously being developed and explored. ...
متن کاملMarkov chain Monte Carlo for Bayesian inference
The chord length transform (CLT) is a useful tool to analyze fibre structures. Assuming e.g. arandom process of straight fibres then a realization of such a process can be observed in a binaryimage. The CLT maps to each point in the foreground of a binary image and to each direction thelength of the related chord, where the chord is the connecting part of a line in the direction...
متن کاملBayesian phylogenetic inference via Markov chain Monte Carlo methods.
We derive a Markov chain to sample from the posterior distribution for a phylogenetic tree given sequence information from the corresponding set of organisms, a stochastic model for these data, and a prior distribution on the space of trees. A transformation of the tree into a canonical cophenetic matrix form suggests a simple and effective proposal distribution for selecting candidate trees cl...
متن کاملBayesian Analysis of Spectral Mixture Data using Markov Chain Monte Carlo Methods
This paper presents an original method for the analysis of multicomponent spectral data sets. The proposed algorithm is based on Bayesian estimation theory and Markov Chain Monte Carlo (MCMC) methods. Resolving spectral mixture analysis aims at recovering the unknown component spectra and at assessing the concentrations of the underlying species in the mixtures. In addition to non-negativity co...
متن کاملOn Markov chain Monte Carlo methods for tall data
Markov chain Monte Carlo methods are often deemed too computationally intensive to be of any practical use for big data applications, and in particular for inference on datasets containing a large number n of individual data points, also known as tall datasets. In scenarios where data are assumed independent, various approaches to scale up the MetropolisHastings algorithm in a Bayesian inferenc...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Annual Review of Astronomy and Astrophysics
سال: 2017
ISSN: 0066-4146,1545-4282
DOI: 10.1146/annurev-astro-082214-122339